Analysis of waveforms is particularly applicable to sound waves and to the use of such analysis in hearing aids and speech recognition systems. Some sound wave processors begin the process of analysis by dividing the speech wave into separate frequency channels, either using Fourier transform methods or a filterbank that mimics the filtering encountered in the human auditory system to a greater or lesser degree.
One of the major problems encountered with the use of a filterbank is that the output of the filterbank incorporates not only details of the input speech wave, the source, but also features which are characteristics of the filterbank itself. The features of the output of a filterbank which are caused inherently by the filterbank include the spectral and temporal broadening and smearing of the output relative to the input.
Matched filters are known which counteract the effects caused inherently by a filterbank however such matched filters do not counteract the effects caused in all dimensions of the filterbank i.e. both temporally and spectrally. Furthermore the matched filters replicate but reverse the filterbank effects and are not sensitive or responsive to the actual information due to the source in the output of the filterbank.
It is also necessary for effective speech analysis that unwanted `noise` which is detected initially is limited or removed from the output of the filterbank and that more important features of the speech wave under analysis are accentuated.
The dynamic range of signals presented to the filterbank is enormous. As a result, the second stage of any analysis commonly involves compression of the dynamic range. Although the compression is often essential, it causes two further problems: it broadens features in the output of the filterbank and reduces the contrast between two adjacent features.